Differential Associations of IL-4 With Hippocampal Subfields in Mild Cognitive Impairment and Alzheimer's Disease

Background/Aims: We aimed to assess the association between in volumetric measures of hippocampal sub-regions - in healthy older controls (HC), subjects with mild cognitive impairment (MCI) and AD- with circulating levels of IL-4. Methods: From AddNeuroMed Project 113 HC, 101 stable MCI (sMCI), 22 converter MCI (cMCI) and 119 AD were included. Hippocampal subfield volumes were analyzed using Freesurfer 6.0.0 on high-resolution sagittal 3D-T1W MP-RAGE acquisitions. Plasmatic IL-4 was measured using ELISA assay. Results: IL-4 was found to be (a) positively associate with left subiculum volume (β = 0.226, p = 0.037) in sMCI and (b) negatively associate with left subiculum volume (β = -0.253, p = 0.011) and left presubiculum volume (β = -0.257, p = 0.011) in AD. Conclusion: Our results indicate a potential neuroprotective effect of IL-4 on the areas of the hippocampus more vulnerable to aging and neurodegeneration.

A Multi-Cohort Study of ApoE ɛ4 and Amyloid-β Effects on the Hippocampus in Alzheimer's Disease

The apolipoprotein E (APOE) gene has been consistently shown to modulate the risk of Alzheimer’s disease (AD). Here, using an AD and normal aging dataset primarily consisting of three AD multi-center studies (n = 1,781), we compared the effect of APOE and amyloid-β (Aβ) on baseline hippocampal volumes in AD patients, mild cognitive impairment (MCI) subjects, and healthy controls. A large sample of healthy adolescents (n = 1,387) was also used to compare hippocampal volumes between APOE groups. Subjects had undergone a magnetic resonance imaging (MRI) scan and APOE genotyping. Hippocampal volumes were processed using FreeSurfer. In the AD and normal aging dataset, hippocampal comparisons were performed in each APOE group and in ɛ4 carriers with positron emission tomography (PET) Aβ who were dichotomized (Aβ+/Aβ–) using previous cut-offs. We found a linear reduction in hippocampal volumes with ɛ4 carriers possessing the smallest volumes, ɛ3 carriers possessing intermediate volumes, and ɛ2 carriers possessing the largest volumes. Moreover, AD and MCI ɛ4 carriers possessed the smallest hippocampal volumes and control ɛ2 carriers possessed the largest hippocampal volumes. Subjects with both APOE ɛ4 and Aβ positivity had the lowest hippocampal volumes when compared to Aβ- ɛ4 carriers, suggesting a synergistic relationship between APOE ɛ4 and Aβ. However, we found no hippocampal volume differences between APOE groups in healthy 14-year-old adolescents. Our findings suggest that the strongest neuroanatomic effect of APOE ɛ4 on the hippocampus is observed in AD and groups most at risk of developing the disease, whereas hippocampi of old and young healthy individuals remain unaffected.

A Mathematical Model for the Estimation of Human Embryonic and Fetal Age

Precise determination of donor age in human embryonic and fetal tissue is crucial for cell transplantation due to the existence of distinct time windows within which successful grafting is possible. This study demonstrates that between 4-12 wk postconception embryonic and fetal age can be estimated based on various morphometric parameters measured on a routine basis in suction abortion material. The greatest length, the neck-rump length, the foot length, and the proximal and distal arm and leg length were correlated with the anamnestic and ultra-sonographically estimated age. Multivariate regression analyses showed a linear correlation between age and the logarithmic value of the various morphometric parameters. The best correlation was found for a mathematical model combining the limb parameters (r = 0.904; p < 0.001; n −37). A prospective follow-up study (n = 40) was carried out to test the validity of the mathematical model. A high correlation was found between the calculated age and the estimated age based on anamnestic data (r = 0.749, p < 0.001). Outliers due to errors in the anamnestic data were readily identified by comparing anamnestic with calculated age. This method allows determination of embryonic and fetal age within and beyond the age group of the Carnegie classification and may, therefore, be useful for the needs of experimental and clinical cell transplantation.

Disrupted Network Topology in Patients with Stable and Progressive Mild Cognitive Impairment and Alzheimer's Disease

Recent findings suggest that Alzheimer's disease (AD) is a disconnection syndrome characterized by abnormalities in large-scale networks. However, the alterations that occur in network topology during the prodromal stages of AD, particularly in patients with stable mild cognitive impairment (MCI) and those that show a slow or faster progression to dementia, are still poorly understood. In this study, we used graph theory to assess the organization of structural MRI networks in stable MCI (sMCI) subjects, late MCI converters (lMCIc), early MCI converters (eMCIc), and AD patients from 2 large multicenter cohorts: ADNI and AddNeuroMed. Our findings showed an abnormal global network organization in all patient groups, as reflected by an increased path length, reduced transitivity, and increased modularity compared with controls. In addition, lMCIc, eMCIc, and AD patients showed a decreased path length and mean clustering compared with the sMCI group. At the local level, there were nodal clustering decreases mostly in AD patients, while the nodal closeness centrality detected abnormalities across all patient groups, showing overlapping changes in the hippocampi and amygdala and nonoverlapping changes in parietal, entorhinal, and orbitofrontal regions. These findings suggest that the prodromal and clinical stages of AD are associated with an abnormal network topology.

Automated Hippocampal Subfield Measures as Predictors of Conversion from Mild Cognitive Impairment to Alzheimer's Disease in Two Independent Cohorts

Previous studies have shown that hippocampal subfields may be differentially affected by Alzheimer’s disease (AD). This study used an automated analysis technique and two large cohorts to (1) investigate patterns of subfield volume loss in mild cognitive impairment (MCI) and AD, (2) determine the pattern of subfield volume loss due to age, gender, education, APOE ε4 genotype, and neuropsychological test scores, (3) compare combined subfield volumes to hippocampal volume alone at discriminating between AD and healthy controls (HC), and predicting future MCI conversion to AD at 12 months. 1,069 subjects were selected from the AddNeuroMed and Alzheimer’s disease neuroimaging initiative (ADNI) cohorts. Freesurfer was used for automated segmentation of the hippocampus and hippocampal subfields. Orthogonal partial least squares to latent structures (OPLS) was used to train models on AD and HC subjects using one cohort for training and the other for testing and the combined cohort was used to predict MCI conversion. MANCOVA and linear regression analyses showed multiple subfield volumes including Cornu Ammonis 1 (CA1), subiculum and presubiculum were atrophied in AD and MCI and were related to age, gender, education, APOE ε4 genotype, and neuropsychological test scores. For classifying AD from HC, combined subfield volumes achieved comparable classification accuracy (81.7 %) to total hippocampal (80.7 %), subiculum (81.2 %) and presubiculum (80.6 %) volume. For predicting MCI conversion to AD combined subfield volumes and presubiculum volume were more accurate (81.1 %) than total hippocampal volume. (76.7 %).

Influence of age, disease onset and ApoE4 on visual medial temporal lobe atrophy cut-offs

BACKGROUND

Visual assessment of medial temporal lobe atrophy (MTA; range 0-4, from no atrophy to increasing atrophy of the choroid fissure, temporal horns and hippocampus) is a sensitive radiological marker of Alzheimer's disease (AD). One of the critical elements for visual MTA assessment is the cut-off score that determines deviation from normality.

METHODS

In this study, we assessed the sensitivity and specificity of different MTA cut-off scores to classify control subjects, individuals with mild cognitive impairment (MCI) and AD patients from two large independent cohorts, AddNeuroMed and Alzheimer's Disease Neuroimaging Initiative. Of note, we evaluated the effects of clinical, demographic and genetic variables on the classification performance according to the different cut-offs.

RESULTS

A cut-off of ≥1.5 based on the mean MTA scores of both hemispheres showed higher sensitivity in classifying patients with AD (84.5%) and MCI subjects (75.8%) who converted to dementia compared to an age-dependent cut-off. The age-dependent cut-off showed higher specificity or ability to correctly identify control subjects (83.2%) and those with MCI who remained stable (65.5%). Increasing age, early-onset disease and absence of the ApoE ε4 allele had a stronger influence on classifications using the ≥1.5 cut-off. Above 75 years of age, an alternative cut-off of ≥2.0 should be applied to achieve a classification accuracy for both patients with AD and control subjects that is clinically useful.

CONCLUSION

Clinical, demographic and genetic variables can influence the classification of MTA cut-off scores, leading to misdiagnosis in some cases. These variables, in addition to the differential sensitivity and specificity of each cut-off, should be carefully considered when performing visual MTA assessment.

CERAD Neuropsychological Total Scores Reflect Cortical Thinning in Prodromal Alzheimer's Disease

Background

Sensitive cognitive global scores are beneficial in screening and monitoring for prodromal Alzheimer's disease (AD). Early cortical changes provide a novel opportunity for validating established cognitive total scores against the biological disease markers.

Methods

We examined how two different total scores of the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) battery and the Mini-Mental State Examination (MMSE) are associated with cortical thickness (CTH) in mild cognitive impairment (MCI) and prodromal AD. Cognitive and magnetic resonance imaging (MRI) data of 22 progressive MCI, 78 stable MCI, and 98 control subjects, and MRI data of 103 AD patients of the prospective multicenter study were analyzed.

Results

CERAD total scores correlated with mean CTH more strongly (r = 0.34-0.38, p < 0.001) than did MMSE (r = 0.19, p = 0.01). Of those vertex clusters that showed thinning in progressive MCI, 60-75% related to the CERAD total scores and 3% to the MMSE.

Conclusion

CERAD total scores are sensitive to the CTH signature of prodromal AD, which supports their biological validity in detecting early disease-related cognitive changes.

Entorhinal cortex thickness predicts cognitive decline in Alzheimer's disease

Biomarkers for Alzheimer's disease (AD) based on non-invasive methods are highly desirable for diagnosis, disease progression, and monitoring therapeutics. We aimed to study the use of hippocampal volume, entorhinal cortex (ERC) thickness, and whole brain volume (WBV) as predictors of cognitive change in patients with AD. 120 AD subjects, 106 mild cognitive impairment (MCI), and 99 non demented controls (NDC) from the multi-center pan-European AddNeuroMed study underwent MRI scanning at baseline and clinical evaluations at quarterly follow-up up to 1 year. The rate of cognitive decline was estimated using cognitive outcomes, Mini-Mental State Examination (MMSE) and Alzheimer disease assessment scale-cognitive (ADAS-cog) by fitting a random intercept and slope model. AD subjects had smaller ERC thickness and hippocampal and WBV volumes compared to MCI and NDC subjects. Within the AD group, ERC > WBV was significantly associated with baseline cognition (MMSE, ADAS-cog) and disease severity (Clinical Dementia Rating). Baseline ERC thickness was associated with both longitudinal MMSE and ADAS-cog score changes and WBV with ADAS-cog decline. These data indicate that AD subjects with thinner ERC had lower baseline cognitive scores, higher disease severity, and predicted greater subsequent cognitive decline at one year follow up. ERC is a region known to be affected early in the disease. Therefore, the rate of atrophy in this structure is expected to be higher since neurodegeneration begins earlier. Focusing on structural analyses that predict decline can identify those individuals at greatest risk for future cognitive loss. This may have potential for increasing the efficacy of early intervention.

Classification and prediction of clinical diagnosis of Alzheimer's disease based on MRI and plasma measures of α-/γ-tocotrienols and γ-tocopherol

BACKGROUND

The aim of this study was to evaluate the accuracy of combined structural magnetic resonance imaging (MRI) measures and plasma levels of vitamin E forms, including all eight natural vitamin E congeners (four tocopherols and four tocotrienols) and markers of vitamin E oxidative/nitrosative damage, in differentiating individuals with Alzheimer's disease (AD) and mild cognitive impairment (MCI) from cognitively intact control (CTL) subjects.

METHODS

Overall, 81 patients with AD, 86 with MCI and 86 CTL individuals were enrolled from the longitudinal multicentre AddNeuroMed study. MRI and plasma vitamin E data were acquired at baseline. MRI scans were analysed using Freesurfer, an automated segmentation scheme which generates regional volume and cortical thickness measures. Orthogonal partial least squares to latent structures (OPLS), a multivariate data analysis technique, was used to analyse MRI and vitamin E measures in relation to AD and MCI diagnosis.

RESULTS

The joint evaluation of MRI and plasma vitamin E measures enhanced the accuracy of differentiating individuals with AD and MCI from CTL subjects: 98.2% (sensitivity 98.8%, specificity 97.7%) for AD versus CTL, and 90.7% (sensitivity 91.8%, specificity 89.5%) for MCI versus CTL. This combination of measures also identified 85% of individuals with MCI who converted to clinical AD at follow-up after 1 year.

CONCLUSIONS

Plasma levels of tocopherols and tocotrienols together with automated MRI measures can help to differentiate AD and MCI patients from CTL subjects, and to prospectively predict MCI conversion into AD. Our results suggest the potential role of nutritional biomarkers detected in plasma-tocopherols and tocotrienols-as indirect indicators of AD pathology, and the utility of a multimodality approach.

Different multivariate techniques for automated classification of MRI data in Alzheimer's disease and mild cognitive impairment

Automated structural magnetic resonance imaging (MRI) processing pipelines and different multivariate techniques are gaining popularity for Alzheimer's disease (AD) research. We used four supervised learning methods to classify AD patients and controls (CTL) and to prospectively predict the conversion of mild cognitive impairment (MCI) to AD from baseline MRI data. A total of 345 participants from the AddNeuroMed cohort were included in this study; 116 AD patients, 119 MCI patients and 110 CTL individuals. High resolution sagittal 3D MP-RAGE datasets were acquired and MRI data were processed using FreeSurfer. We explored the classification ability of orthogonal projections to latent structures (OPLS), decision trees (Trees), artificial neural networks (ANN) and support vector machines (SVM). Applying 10-fold cross-validation demonstrated that SVM and OPLS were slightly superior to Trees and ANN, although not statistically significant for distinguishing between AD and CTL. The classification experiments resulted in up to 83% sensitivity and 87% specificity for the best techniques. For the prediction of conversion of MCI patients at baseline to AD at 1-year follow-up, we obtained an accuracy of up to 86%. The value of the multivariate models derived from the classification of AD vs. CTL was shown to be robust and efficient in the identification of MCI converters.

An MRI-based index to measure the severity of Alzheimer's disease-like structural pattern in subjects with mild cognitive impairment

BACKGROUND

Structural magnetic resonance imaging (MRI) is sensitive to neurodegeneration and can be used to estimate the risk of converting to Alzheimer's disease (AD) in individuals with mild cognitive impairment (MCI). Brain changes in AD and prodromal AD involve a pattern of widespread atrophy. The use of multivariate analysis algorithms could enable the development of diagnostic tools based on structural MRI data. In this study, we investigated the possibility of combining multiple MRI features in the form of a severity index.

METHODS

We used baseline MRI scans from two large multicentre cohorts (AddNeuroMed and ADNI). On the basis of volumetric and cortical thickness measures at baseline with AD cases and healthy control (CTL) subjects as training sets, we generated an MRI-based severity index using the method of orthogonal projection to latent structures (OPLS). The severity index tends to be close to 1 for AD patients and 0 for CTL subjects. Values above 0.5 indicate a more AD-like pattern. The index was then estimated for subjects with MCI, and the accuracy of classification was investigated.

RESULTS

Based on the data at follow-up, 173 subjects converted to AD, of whom 112 (64.7%) were classified as AD-like and 61 (35.3%) as CTL-like.

CONCLUSION

We found that joint evaluation of multiple brain regions provided accurate discrimination between progressive and stable MCI, with better performance than hippocampal volume alone, or a limited set of features. A major challenge is still to determine optimal cut-off points for such parameters and to compare their relative reliability.

Alzheimer's disease biomarker discovery using in silico literature mining and clinical validation

Background

Alzheimer’s Disease (AD) is the most widespread form of dementia in the elderly but despite progress made in recent years towards a mechanistic understanding, there is still an urgent need for disease modification therapy and for early diagnostic tests. Substantial international efforts are being made to discover and validate biomarkers for AD using candidate analytes and various data-driven 'omics' approaches. Cerebrospinal fluid is in many ways the tissue of choice for biomarkers of brain disease but is limited by patient and clinician acceptability, and increasing attention is being paid to the search for blood-based biomarkers. The aim of this study was to use a novel in silico approach to discover a set of candidate biomarkers for AD.

Methods

We used an in silico literature mining approach to identify potential biomarkers by creating a summarized set of assertional metadata derived from relevant legacy information. We then assessed the validity of this approach using direct assays of the identified biomarkers in plasma by immunodetection methods.

Results

Using this in silico approach, we identified 25 biomarker candidates, at least three of which have subsequently been reported to be altered in blood or CSF from AD patients. Two further candidate biomarkers, indicated from the in silico approach, were choline acetyltransferase and urokinase-type plasminogen activator receptor. Using immunodetection, we showed that, in a large sample set, these markers are either altered in disease or correlate with MRI markers of atrophy.

Conclusions

These data support as a proof of concept the use of data mining and in silico analyses to derive valid biomarker candidates for AD and, by extension, for other disorders.

Development of a small D-enantiomeric Alzheimer's amyloid-β binding peptide ligand for future in vivo imaging applications

Alzheimer’s disease (AD) is a devastating disease affecting predominantly the aging population. One of the characteristic pathological hallmarks of AD are neuritic plaques, consisting of amyloid-β peptide (Aβ). While there has been some advancement in diagnostic classification of AD patients according to their clinical severity, no fully reliable method for pre-symptomatic diagnosis of AD is available. To enable such early diagnosis, which will allow the initiation of treatments early in the disease progress, neuroimaging tools are under development, making use of Aβ-binding ligands that can visualize amyloid plaques in the living brain. Here we investigate the properties of a newly designed series of D-enantiomeric peptides which are derivatives of ACI-80, formerly called D1, which was developed to specifically bind aggregated Aβ1–42. We describe ACI-80 derivatives with increased stability and Aβ binding properties, which were characterized using surface plasmon resonance and enzyme-linked immunosorbent assays. The specific interactions of the lead compounds with amyloid plaques were validated by ex vivo immunochemistry in transgenic mouse models of AD. The novel compounds showed increased binding affinity and are promising candidates for further development into in vivo imaging compounds.

Magnetic resonance imaging and magnetic resonance spectroscopy for detection of early Alzheimer's disease

Alzheimer's disease is the most common form of neurodegenerative disorder and early detection is of great importance if new therapies are to be effectively administered. We have investigated whether the discrimination between early Alzheimer's disease (AD) and elderly healthy control subjects can be improved by adding magnetic resonance spectroscopy (MRS) measures to magnetic resonance imaging (MRI) measures. In this study 30 AD patients and 36 control subjects were included. High resolution T1-weighted axial magnetic resonance images were obtained from each subject. Automated regional volume segmentation and cortical thickness measures were determined for the images. 1H MRS was acquired from the hippocampus and LCModel was used for metabolic quantification. Altogether, this yielded 58 different volumetric, cortical thickness and metabolite ratio variables which were used for multivariate analysis to distinguish between subjects with AD and Healthy controls. Combining MRI and MRS measures resulted in a sensitivity of 97% and a specificity of 94% compared to using MRI or MRS measures alone (sensitivity: 87%, 76%, specificity: 86%, 83% respectively). Adding the MRS measures to the MRI measures more than doubled the positive likelihood ratio from 6 to 17. Adding MRS measures to a multivariate analysis of MRI measures resulted in significantly better classification than using MRI measures alone. The method shows strong potential for discriminating between Alzheimer's disease and controls.

Education increases reserve against Alzheimer's disease--evidence from structural MRI analysis

Introduction

The aim of this study was to determine whether years of schooling influences regional cortical thicknesses and volumes in Alzheimer’s disease (AD), mild cognitive impairment (MCI), and healthy age-matched controls.

Methods

Using an automated image analysis pipeline, 33 regional cortical thickness and 15 regional volumes measures from MRI images were determined in 121 subjects with MCI, 121 patients with AD, and 113 controls from AddNeuroMed study. Correlations with years of schooling were determined and more highly and less highly educated subjects compared, controlling for intracranial volume, age, gender, country of origin, cognitive status, and multiple testing.

Results

After controlling for confounding factors and multiple testing, in the control group, subjects with more education had larger regional cortical thickness in transverse temporal cortex, insula, and isthmus of cingulate cortex than subjects with less education. However, in the AD group, the subjects with more education had smaller regional cortical thickness in temporal gyrus, inferior and superior parietal gyri, and lateral occipital cortex than the subjects with less education. No significant difference was found in the MCI group.

Conclusion

Education may increase regional cortical thickness in healthy controls, leading to increased brain reserve, as well as helping AD patients to cope better with the effects of brain atrophy by increasing cognitive reserve.

Plasma biomarkers of brain atrophy in Alzheimer's disease

Peripheral biomarkers of Alzheimer's disease (AD) reflecting early neuropathological change are critical to the development of treatments for this condition. The most widely used indicator of AD pathology in life at present is neuroimaging evidence of brain atrophy. We therefore performed a proteomic analysis of plasma to derive biomarkers associated with brain atrophy in AD. Using gel based proteomics we previously identified seven plasma proteins that were significantly associated with hippocampal volume in a combined cohort of subjects with AD (N = 27) and MCI (N = 17). In the current report, we validated this finding in a large independent cohort of AD (N = 79), MCI (N = 88) and control (N = 95) subjects using alternative complementary methods—quantitative immunoassays for protein concentrations and estimation of pathology by whole brain volume. We confirmed that plasma concentrations of five proteins, together with age and sex, explained more than 35% of variance in whole brain volume in AD patients. These proteins are complement components C3 and C3a, complement factor-I, γ-fibrinogen and alpha-1-microglobulin. Our findings suggest that these plasma proteins are strong predictors of in vivo AD pathology. Moreover, these proteins are involved in complement activation and coagulation, providing further evidence for an intrinsic role of these pathways in AD pathogenesis.

Fluorine-18 labeling of three novel D-peptides by conjugation with N-succinimidyl-4-[18F]fluorobenzoate and preliminary examination by postmortem whole-hemisphere human brain autoradiography

INTRODUCTION

β-Amyloid (Aβ) plaques and neurofibrillary tangles are the main characteristics of Alzheimer's disease (AD). Positron emission tomography (PET), a high-resolution, sensitive, and noninvasive imaging technique, has been widely utilized in visualizing the localization of plaques and tangles and thereby distinguishing between AD and healthy controls. A small 12-mer D-enantiomeric peptide (amino acid sequence=QSHYRHISPAQV), denoted as D1, has high binding affinity to Aβ in vitro in the sub-micromolar range, and consequently, its radiolabeled analogues have a potential as radioligands for visualizing amyloid plaques in vivo by PET.

AIM

The aims of the present work were to develop three different potent D1 derivative peptides labeled with fluorine-18 and to examine them in the AD and control postmortem human brain by autoradiography (ARG).

METHODS

Three different D1 derivative peptides were radiolabeled with fluorine-18 ([(18)F]ACI-87, [(18)F]ACI-88, [(18)F]ACI-89) using the prosthetic group N-succinimidyl-4-[(18)F]fluorobenzoate ([(18)F]SFB) and purified by high performance liquid chromatography (HPLC). Preliminary ARG measurements were performed in AD and control brains.

RESULTS

The three fluorine-18-labeled d-peptides were obtained in a total synthesis time of 140 min with radiochemical purity higher than 98%. The specific radioactivities of the three different D1 derivative peptides were between 9 and 113 GBq/μmol. ARG demonstrated a higher radioligand uptake in the cortical gray matter and the hippocampus in the AD brain as compared to age-matched control brain.

CONCLUSIONS

Fluorine-18 labeling of the three novel D1 derivative peptides using [(18)F]SFB was successfully accomplished. Higher contrast between AD and control brain slices demonstrates their potential applicability for further use in vivo by PET.

Genome-wide association with MRI atrophy measures as a quantitative trait locus for Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with considerable evidence suggesting an initiation of disease in the entorhinal cortex and hippocampus and spreading thereafter to the rest of the brain. In this study, we combine genetics and imaging data obtained from the Alzheimer's Disease Neuroimaging Initiative and the AddNeuroMed study. To identify genetic susceptibility loci for AD, we conducted a genome-wide study of atrophy in regions associated with neurodegeneration in this condition. We identified one single-nucleotide polymorphism (SNP) with a disease-specific effect associated with entorhinal cortical volume in an intron of the ZNF292 gene (rs1925690; P-value=2.6 × 10(-8); corrected P-value for equivalent number of independent quantitative traits=7.7 × 10(-8)) and an intergenic SNP, flanking the ARPP-21 gene, with an overall effect on entorhinal cortical thickness (rs11129640; P-value=5.6 × 10(-8); corrected P-value=1.7 × 10(-7)). Gene-wide scoring also highlighted PICALM as the most significant gene associated with entorhinal cortical thickness (P-value=6.7 × 10(-6)).

Combining MRI and MRS to distinguish between Alzheimer's disease and healthy controls

Alzheimer's disease (AD) is the most common neurodegenerative disorder among the elderly, and early detection is of great importance if new therapies are to be effectively administered. We have used multivariate data analysis (orthogonal partial least squares to latent structures (OPLS) analysis) to investigate whether the discrimination between AD and elderly healthy control subjects can be improved by adding magnetic resonance spectroscopy (MRS) measures to magnetic resonance imaging (MRI). In this study, 30 AD patients and 36 control subjects were included (mean (SD) age=77(5) and 77(5) years, MMSE=23(4) and 29(1) respectively). High resolution T1-weighted axial magnetic resonance images were obtained from each subject. Automated regional volume segmentation and cortical thickness measures were determined for the images. 1H MRS was acquired from the hippocampus and LCModel was used for metabolite quantification. Altogether, this yielded 54 different volumetric, cortical thickness and metabolite ratio variables which were used for multivariate analysis. All analyses were performed using seven-fold-cross-validation. Combining MRI and MRS measures resulted in a sensitivity of 97% and a specificity of 94% compared to using MRI or MRS measures alone (sensitivity: 93%, 76%, specificity: 86%, 83% respectively). Adding the MRS measures to the MRI measures more than doubled the positive likelihood ratio from 7 to 17. Adding MRS measures to a multivariate analysis of MRI measures resulted in significantly better classification than using MRI measures alone. The OPLS method shows strong potential for discriminating between Alzheimer's disease and controls.

Sensitivity and specificity of medial temporal lobe visual ratings and multivariate regional MRI classification in Alzheimer's disease

Background

Visual assessment rating scales for medial temporal lobe (MTL) atrophy have been used by neuroradiologists in clinical practice to aid the diagnosis of Alzheimer's disease (AD). Recently multivariate classification methods for magnetic resonance imaging (MRI) data have been suggested as alternative tools. If computerized methods are to be implemented in clinical practice they need to be as good as, or better than experienced neuroradiologists and carefully validated. The aims of this study were: (1) To compare the ability of MTL atrophy visual assessment rating scales, a multivariate MRI classification method and manually measured hippocampal volumes to distinguish between subjects with AD and healthy elderly controls (CTL). (2) To assess how well the three techniques perform when predicting future conversion from mild cognitive impairment (MCI) to AD.

Methods

High resolution sagittal 3D T1w MP-RAGE datasets were acquired from 75 AD patients, 101 subjects with MCI and 81 CTL from the multi-centre AddNeuroMed study. An automated analysis method was used to generate regional volume and regional cortical thickness measures, providing 57 variables for multivariate analysis (orthogonal partial least squares to latent structures using seven-fold cross-validation). Manual hippocampal measurements were also determined for each subject. Visual rating assessment of MTL atrophy was performed by an experienced neuroradiologist according to the approach of Scheltens et al.

Results

We found prediction accuracies for distinguishing between AD and CTL of 83% for multivariate classification, 81% for the visual rating assessments and 89% for manual measurements of total hippocampal volume. The three different techniques showed similar accuracy in predicting conversion from MCI to AD at one year follow-up.

Conclusion

Visual rating assessment of the MTL gave similar prediction accuracy to multivariate classification and manual hippocampal volumes. This suggests a potential future role for computerized methods as a complement to clinical assessment of AD.

AddNeuroMed and ADNI: similar patterns of Alzheimer's atrophy and automated MRI classification accuracy in Europe and North America

The European Union AddNeuroMed program and the US-based Alzheimer Disease Neuroimaging Initiative (ADNI) are two large multi-center initiatives designed to collect and validate biomarker data for Alzheimer's disease (AD). Both initiatives use the same MRI data acquisition scheme. The current study aims to compare and combine magnetic resonance imaging (MRI) data from the two study cohorts using an automated image analysis pipeline and a multivariate data analysis approach. We hypothesized that the two cohorts would show similar patterns of atrophy, despite demographic differences and could therefore be combined. MRI scans were analyzed from a total of 1074 subjects (AD=295, MCI=444 and controls=335) using Freesurfer, an automated segmentation scheme which generates regional volume and regional cortical thickness measures which were subsequently used for multivariate analysis (orthogonal partial least squares to latent structures (OPLS)). OPLS models were created for the individual cohorts and for the combined cohort to discriminate between AD patients and controls. The ADNI cohort was used as a replication dataset to validate the model created for the AddNeuroMed cohort and vice versa. The combined cohort model was used to predict conversion to AD at baseline of MCI subjects at 1 year follow-up. The AddNeuroMed, the ADNI and the combined cohort showed similar patterns of atrophy and the predictive power was similar (between 80 and 90%). The combined model also showed potential in predicting conversion from MCI to AD, resulting in 71% of the MCI converters (MCI-c) from both cohorts classified as AD-like and 60% of the stable MCI subjects (MCI-s) classified as control-like. This demonstrates that the methods used are robust and that large data sets can be combined if MRI imaging protocols are carefully aligned.

Apathy and cortical atrophy in Alzheimer's disease

OBJECTIVES

Apathy has been reported as the most prevalent behavioural symptom experienced in Alzheimer's disease (AD), associated with greater functional decline and caregiver distress. The aim of the current study was to investigate structural correlates of apathy in AD using magnetic resonance imaging (MRI) regional volume and regional cortical thickness measures.

METHODS

Semi-structured interviews were conducted with 111 AD patients and their caregivers as part of the European multi-centre study AddNeuroMed. Apathy was measured using the apathy domain of the Neuropsychiatric Inventory (NPI). All AD patients were scanned using a 1.5T MRI scanner and the images analysed using an automated analysis pipeline.

RESULTS

We found apathy to be the most prevalent neuropsychiatric symptom occurring in 57% of patients. Apathetic patients had significantly greater cortical thinning in left caudal anterior cingulate cortex (ACC) and left lateral orbitofrontal cortex (OFC), as well as left superior and ventrolateral frontal regions, than those without apathy symptoms.

CONCLUSIONS

Apathy is mediated by frontocortical structures but this is specific to the left hemisphere at least for patients in the mild to moderate stages of AD.

Automated hippocampal shape analysis predicts the onset of dementia in mild cognitive impairment

The hippocampus is involved at the onset of the neuropathological pathways leading to Alzheimer's disease (AD). Individuals with mild cognitive impairment (MCI) are at increased risk of AD. Hippocampal volume has been shown to predict which MCI subjects will convert to AD. Our aim in the present study was to produce a fully automated prognostic procedure, scalable to high throughput clinical and research applications, for the prediction of MCI conversion to AD using 3D hippocampal morphology. We used an automated analysis for the extraction and mapping of the hippocampus from structural magnetic resonance scans to extract 3D hippocampal shape morphology, and we then applied machine learning classification to predict conversion from MCI to AD. We investigated the accuracy of prediction in 103 MCI subjects (mean age 74.1 years) from the longitudinal AddNeuroMed study. Our model correctly predicted MCI conversion to dementia within a year at an accuracy of 80% (sensitivity 77%, specificity 80%), a performance which is competitive with previous predictive models dependent on manual measurements. Categorization of MCI subjects based on hippocampal morphology revealed more rapid cognitive deterioration in MMSE scores (p<0.01) and CERAD verbal memory (p<0.01) in those subjects who were predicted to develop dementia relative to those predicted to remain stable. The pattern of atrophy associated with increased risk of conversion demonstrated initial degeneration in the anterior part of the cornus ammonis 1 (CA1) hippocampal subregion. We conclude that automated shape analysis generates sensitive measurements of early neurodegeneration which predates the onset of dementia and thus provides a prognostic biomarker for conversion of MCI to AD.

APOE ε2 allele is associated with larger regional cortical thicknesses and volumes

BACKGROUND

The protective effect of the apolipoprotein E (APOE) ε2 allele against Alzheimer's disease (AD) is controversial.

OBJECTIVE

Our purpose was to clarify if the ε2 allele affects regional cortical thicknesses and volumes.

METHODS

Regional cortical thicknesses and volumes were measured with an automated pipeline in 109 subjects with mild cognitive impairment, 114 AD patients and 105 age-matched healthy controls.

RESULTS

In the mild cognitive impairment group, the ε2 carriers had thicker regional cortices at the transverse temporal cortex and parahippocampal gyrus than the subjects with ε3/ε3, and a larger cerebral gray matter and smaller lateral ventricles than the ε3/ε3 and ε4 carriers. In the AD group, the ε2 carriers had significantly thicker entorhinal and transverse temporal cortices, a larger whole cerebral gray matter, and smaller lateral ventricles than the subjects with the ε3/ε3 genotype, and a significantly thicker entorhinal cortex and larger cerebral gray matter than ε4 carriers. No APOE2 effect was found in the control group.

CONCLUSION

The APOE ε2 allele is associated with larger regional cortical thicknesses and volumes in mild cognitive impairment and AD.

1H-MRS in spinal cord injury: acute and chronic metabolite alterations in rat brain and lumbar spinal cord

A variety of tests of sensorimotor function are used to characterize outcome after experimental spinal cord injury (SCI). These tests typically do not provide information about chemical and metabolic processes in the injured CNS. Here, we used (1) H-magnetic resonance spectroscopy (MRS) to monitor long-term and short-term chemical changes in the CNS in vivo following SCI. The investigated areas were cortex, thalamus/striatum and the spinal cord distal to injury. In cortex, glutamate (Glu) decreased 1 day after SCI and slowly returned towards normal levels. The combined glutamine (Gln) and Glu signal was similarly decreased in cortex, but increased in the distal spinal cord, suggesting opposite changes of the Glu/Gln metabolites in cortex and distal spinal cord. In lumbar spinal cord, a marked increase of myo-inositol was found 3 days, 14 days and 4 months after SCI. Changes in metabolite concentrations in the spinal cord were also found for choline and N-acetylaspartate. No significant changes in metabolite concentrations were found in thalamus/striatum. Multivariate data analysis allowed separation between rats with SCI and controls for spectra acquired in cortex and spinal cord, but not in thalamus/striatum. Our findings suggest MRS could become a helpful tool to monitor spatial and temporal alterations of metabolic conditions in vivo in the brain and spinal cord after SCI. We provide evidence for dynamic temporal changes at both ends of the neuraxis, cortex cerebri and distal spinal cord, while deep brain areas appear less affected.

Effect of APOE ε4 allele on cortical thicknesses and volumes: the AddNeuroMed study

The apolipoprotein E (APOE) ε4 allele is a risk factor for Alzheimer's disease (AD), but its effect on brain volumes is controversial. We explored the effect of the ε4 allele on regional cortical thickness and volume measurements using an automated pipeline in 111 subjects with mild cognitive impairment (MCI), 115 AD patients, and 107 age-matched healthy controls. The clinical data were used as covariates in the thickness and volume comparisons. The ε4 carriers had significantly smaller volume than non-carriers in caudate (p=0.028) in controls; in amygdala and caudate in the MCI group (p <or= 0.049); and in hippocampus and amygdala in the AD group (p <or= 0.001). In the female subjects, the ε4 carriers had significantly thinner cortical thickness or smaller volume than non-carriers in medial orbitofrontal gyrus and caudate in controls (p <or= 0.014); in amygdala in MCI subjects (p=0.047) and in hippocampus and amygdala in AD patients (p <or= 0.024). However, in the male subjects, there were significant differences in cortical thickness and volume between ε4 carriers and non-carriers in several structures in the MCI group, but no differences in the controls and AD patients. Compared to the non-carriers, the homozygous ε4 carriers showed significant volume loss in hippocampus, deep nuclei, and caudal anterior cingulate cortex in MCI. In the AD group, the homozygous ε4 carriers had significant volume loss in hippocampus and amygdala. We conclude that the APOE ε4 allele modulates regional cortical thickness and volume in relation to diagnostic group and gender. The ε4 allele has a dose-dependent and regionally specific effect on brain structures.

The AddNeuroMed framework for multi-centre MRI assessment of Alzheimer's disease: experience from the first 24 months

OBJECTIVE

To describe the AddNeuroMed imaging framework for multi-centre magnetic resonance imaging (MRI) assessment of longitudinal changes in Alzheimer's disease and report on early results from the first 24 months of the project.

METHODS

A multi-centre study similarly to a faux clinical trial has been established to assess longitudinal MRI changes in Alzheimer disease (AD), mild cognitive impairment (MCI) and healthy control subjects using an image acquisition protocol compatible with Alzheimer disease neuroimaging initiative (ADNI). Comprehensive quality control (QC) measures have been established throughout the study. An intelligent web-accessible database holds details on both the raw images and data processed using a sophisticated image analysis pipeline.

RESULTS

A total of 378 subjects have been recruited (130 AD, 131 MCI, 117 healthy controls) of which a high percentage (97.3%) of the T1-weighted volumes passed the QC criteria. Measurements of normalized whole brain volume and whole brain cortical thickness showed significant differences between AD and controls, AD and MCI and MCI and controls.

CONCLUSIONS

A framework for multi-centre MRI studies of Alzheimer's disease has been established consisting of a harmonized MRI acquisition protocol across centres, rigorous QC at both the sites and central data analysis hub and an automated image analysis pipeline. Early results demonstrate the high quality of the images acquired and the applicability of the automated image analysis techniques employed.

Multivariate analysis of MRI data for Alzheimer's disease, mild cognitive impairment and healthy controls

We have used multivariate data analysis, more specifically orthogonal partial least squares to latent structures (OPLS) analysis, to discriminate between Alzheimer's disease (AD), mild cognitive impairment (MCI) and elderly control subjects combining both regional and global magnetic resonance imaging (MRI) volumetric measures. In this study, 117 AD patients, 122 MCI patients and 112 control subjects (from the AddNeuroMed study) were included. High-resolution sagittal 3D MP-RAGE datasets were acquired from each subject. Automated regional segmentation and manual outlining of the hippocampus were performed for each image. Altogether this yielded volumes of 24 different anatomically defined structures which were used for OPLS analysis. 17 randomly selected AD patients, 12 randomly selected control subjects and the 22 MCI subjects who converted to AD at 1-year follow up were excluded from the initial OPLS analysis to provide a small external test set for model validation. Comparing AD with controls we found a sensitivity of 87% and a specificity of 90% using hippocampal measures alone. Combining both global and regional measures resulted in a sensitivity of 90% and a specificity of 94%. This increase in sensitivity and specificity resulted in an increase of the positive likelihood ratio from 9 to 15. From the external test set, the model predicted 82% of the AD patients and 83% of the control subjects correctly. Finally, 73% of the MCI subjects which converted to AD at 1 year follow-up were shown to resemble AD patients more closely than controls. This method shows potential for distinguishing between different patient groups. Combining the different MRI measures together resulted in a significantly better classification than using them separately. OPLS also shows potential for predicting conversion from MCI to AD.

Association of plasma clusterin concentration with severity, pathology, and progression in Alzheimer disease

CONTEXT

Blood-based analytes may be indicators of pathological processes in Alzheimer disease (AD).

OBJECTIVE

To identify plasma proteins associated with AD pathology using a combined proteomic and neuroimaging approach.

DESIGN

Discovery-phase proteomics to identify plasma proteins associated with correlates of AD pathology. Confirmation and validation using immunodetection in a replication set and an animal model.

SETTING

A multicenter European study (AddNeuroMed) and the Baltimore Longitudinal Study of Aging.

PARTICIPANTS

Patients with AD, subjects with mild cognitive impairment, and healthy controls with standardized clinical assessments and structural neuroimaging.

MAIN OUTCOME MEASURES

Association of plasma proteins with brain atrophy, disease severity, and rate of clinical progression. Extension studies in humans and transgenic mice tested the association between plasma proteins and brain amyloid.

RESULTS

Clusterin/apolipoprotein J was associated with atrophy of the entorhinal cortex, baseline disease severity, and rapid clinical progression in AD. Increased plasma concentration of clusterin was predictive of greater fibrillar amyloid-beta burden in the medial temporal lobe. Subjects with AD had increased clusterin messenger RNA in blood, but there was no effect of single-nucleotide polymorphisms in the gene encoding clusterin with gene or protein expression. APP/PS1 transgenic mice showed increased plasma clusterin, age-dependent increase in brain clusterin, as well as amyloid and clusterin colocalization in plaques.

CONCLUSIONS

These results demonstrate an important role of clusterin in the pathogenesis of AD and suggest that alterations in amyloid chaperone proteins may be a biologically relevant peripheral signature of AD.

AddNeuroMed--the European collaboration for the discovery of novel biomarkers for Alzheimer's disease

There is an urgent need for Alzheimer's disease (AD) biomarkers-especially in the context of clinical trials. Biomarkers for early diagnosis, disease progression, and prediction are most critical, and disease-modification therapy development may depend on the discovery and validation of such markers. AddNeuroMed is a cross European, public/private consortium developed for AD biomarker discovery. We report here the development and design of AddNeuroMed and the progress toward the development of plasma markers. Despite the obstacles to such markers, we have identified a range of markers including CFH and A2M, both of which have been independently replicated. The experience of AddNeuroMed leads us to three overall conclusions. First, collaboration is essential. Second, design is paramount and combining modalities, such as imaging and proteomics, may be informative. Third, animal models are valuable in biomarker research. Most importantly, we have learned that plasma markers are feasible.

MRI measures of Alzheimer's disease and the AddNeuroMed study

Here we describe the AddNeuroMed multicenter magnetic resonance imaging (MRI) study for longitudinal assessment in Alzheimer's disease (AD). The study is similar to a faux clinical trial and has been established to assess longitudinal MRI changes in AD, mild cognitive impairment (MCI), and healthy control subjects using an image acquisition protocol compatible with the Alzheimer's Disease Neuroimaging Initiative (ADNI). The approach consists of a harmonized MRI acquisition protocol across centers, rigorous quality control, a central data analysis hub, and an automated image analysis pipeline. Comprehensive quality control measures have been established throughout the study. An intelligent web-accessible database holds details on both the raw images and data processed using a sophisticated image analysis pipeline. A total of 378 subjects were recruited (130 AD, 131 MCI, 117 healthy controls) of which a high percentage (97.3%) of the T1-weighted volumes passed the quality control criteria. Measurements of normalized whole brain volume, whole brain cortical thickness, and point-by-point group-based cortical thickness measurements, demonstrating the power of the automated image analysis techniques employed, are reported.

Grid infrastructures for computational neuroscience: the neuGRID example

Neuroscience is increasingly making use of statistical and mathematical tools to extract information from images of biological tissues. Computational neuroimaging tools require substantial computational resources and the increasing availability of large image datasets will further enhance this need. Many efforts have been directed towards creating brain image repositories including the recent US Alzheimer Disease Neuroimaging Initiative. Multisite-distributed computing infrastructures have been launched with the goal of fostering shared resources and facilitating data analysis in the study of neurodegenerative diseases. Currently, some Grid- and non-Grid-based projects are aiming to establish distributed e-infrastructures, interconnecting compatible imaging datasets and to supply neuroscientists with the most advanced information and communication technologies tools to study markers of Alzheimer’s and other brain diseases, but they have so far failed to make a difference in the larger neuroscience community. NeuGRID is an Europeon comission-funded effort arising from the needs of the Alzheimer’s disease imaging community, which will allow the collection and archiving of large amounts of imaging data coupled with Grid-based algorithms and sufficiently powered computational resources. The major benefit will be the faster discovery of new disease markers that will be valuable for earlier diagnosis and development of innovative drugs. The initial setup of neuGRID will feature three nodes equipped with supercomputer capabilities and resources of more than 300 processor cores, 300 GB of RAM memory and approximately 20 TB of physical space. The scope of this article is highlights the new perspectives and potential for the study of the neurodegenerative disorders using the emerging Grid technology.

Combination analysis of neuropsychological tests and structural MRI measures in differentiating AD, MCI and control groups--the AddNeuroMed study

To study the ability of neuropsychological tests, manual MRI hippocampal volume measures, regional volume and cortical thickness measures to identify subjects with Alzheimer's disease (AD), mild cognitive impairment (MCI), and healthy age-matched controls. Neuropsychological tests, manual hippocampal volume, automated regional volume and regional cortical thickness measures were performed in 120 AD patients, 120 MCI subjects, and 111 controls. The regional cortical thickness and volumes in MCI subjects were significantly decreased in limbic/paralimbic areas and temporal lobe compared to controls. Atrophy was much more extensive in the AD patients compared to MCI subjects and controls. The combination of neuropsychological tests and volumes revealed the highest accuracy (82% AD vs. MCI; 94% AD vs. control; 83% MCI vs. control). Adding regional cortical thicknesses into the discriminate analysis did not improve accuracy. We conclude that regional cortical thickness and volume measures provide a panoramic view of brain atrophy in AD and MCI subjects. A combination of neuropsychological tests and regional volumes are important when discriminating AD from healthy controls and MCI.

In vivo 1H-magnetic resonance spectroscopy can detect metabolic changes in APP/PS1 mice after donepezil treatment

Background

Donepezil improves cognitive functions in AD patients. Effects on the brain metabolites N-acetyl-L-aspartate, choline and myo-inositol levels have been reported in clinical studies using this drug. The APP/PS1 mouse coexpresses the mutated forms of human β-amyloid precursor protein (APP) and mutated human presenilin 1 (PS1). Consequently, the APP/PS1 mouse model reflects important features of the neurochemical profile in humans. In vivo magnetic resonance spectroscopy (¹H-MRS) was performed in fronto-parietal cortex and hippocampus (ctx/hipp) and in striatum (str). Metabolites were quantified using the LCModel and the final analysis was done using multivariate data analysis. The aim of this study was to investigate if multivariate data analysis could detect changes in the pattern of the metabolic profile after donepezil treatment.

Results

Significant differences were observed in the metabolic pattern of APP/PS1 mice in both str and ctx/hipp before and after donepezil treatment using multivariate data analysis, evidencing a significant treatment effect. A treatment effect was also seen in wild type (wt) mice in str. A significant decrease in the metabolic ratio taurine/creatine (Tau/tCr) was related to donepezil treatment (p < 0.05) in APP/PS1 mice in both brain regions. Furthermore, a significant influence on the choline/creatine (tCho/tCr) level was observed in treated APP/PS1 mice compared to untreated in str (p = 0.011). Finally, there was an increase in glutamate/creatine (Glu/tCr) in str in wt mice treated with donepezil.

Conclusion

Multivariate data analysis can detect changes in the metabolic profile in APP/PS1 mice after donepezil treatment. Effects on several metabolites that are measurable in vivo using MR spectroscopy were observed. Changes in Tau/tCr and tCho/tCr could possibly be related to changed cholinergic activity caused by donepezil treatment.

Age related changes in brain metabolites observed by 1H MRS in APP/PS1 mice

Translational biomarkers in Alzheimer's disease based on non-invasive in vivo methods are highly warranted. (1)H magnetic resonance spectroscopy (MRS) is non-invasive and applicable in vivo in both humans and experimental animals. In vivo(1)H MRS and 3D MRI were performed on brains of double transgenic (tg) mice expressing a double mutant human beta-amyloid precursor protein APP(K670N,M671L) and human mutated presenilin gene PS1M146L, and wild-type (wt) littermates at 2.5, 6.5 and 9 months of age using a 9.4T magnet. For quantification, LCModel was used, and the data were analyzed using multivariate data analysis (MVDA). MVDA evidenced a significant separation, which became more pronounced with age, between tg and wt mice at all time points. While myo-inositol and guanidoacetate were important for group separation in young mice, N-acetylaspartate, glutamate and macrolipids were important for separation of aged tg and wt mice. Volume segmentation revealed that brain and hippocampus were readily smaller in tg as compared to wt mice at the age of 2.5 months. Amyloid plaques were seen in 6.5 and 9 months, but not in 2.5 months old animals. In conclusion, differences in brain metabolites could be accurately depicted in tg and wt mice in vivo by combining MRS with MVDA. First differences in metabolite content were readily seen at 2.5 months, when volume defects in tg mice were present, but no amyloid plaques.